CN111626469B - Fast and slow vehicle driving optimization method for operation energy improvement - Google Patents
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Abstract
The application provides a fast and slow vehicle driving optimization method for improving the running performance, which comprises the steps of dividing passenger types according to line information selected by passengers, constructing a passenger path set, deducing a calculation formula of influence factors of different passenger types for selecting different passenger paths, calculating passenger path selection probability according to the influence factors by using a path comprehensive cost value formula and a Logit model, establishing a double-layer planning model according to different passenger paths and passenger flow distribution on different passenger paths determined by the passenger path selection probability, solving the double-layer planning model by using a particle swarm algorithm, outputting an optimal running scheme, effectively improving the running efficiency of an urban rail transit system, shortening long-distance passenger running time, effectively improving the running performance problems of passenger flow increase and passenger flow space distribution imbalance, and reducing train running cost and passenger running time on the basis of meeting the passenger demands.
Description
Technical Field
The application belongs to the field of optimization of fast and slow vehicle driving schemes, and particularly relates to a fast and slow vehicle driving optimization method for improving running energy.
Background
With the increasing development of urban scale, urban rail transit is becoming the main traffic mode for relieving urban traffic pressure, so research and optimization of train operation schemes are very important, most suburban rail routes are longer, the number of stop stations along the way is large, the degree of unbalanced spatial distribution of passenger flows in the commuting period of the routes is higher, the passenger distance is longer, and the passenger flows with unbalanced spatial and temporal distribution cannot be well distributed in the train operation.
In order to meet the commute time requirements of the rush hour commute passenger flow in the morning and evening, part of cities start to adopt the operation mode of fast and slow vehicles on suburb lines. The reasonable fast and slow car running scheme can effectively improve the running efficiency of the urban rail transit system, so that the fast car can ensure the transport capacity of passenger flows along the stations while shortening the travel time of long-distance passengers, and the optimized fast and slow car running scheme can be used for solving the problem of serious mismatch between the transport capacity of passenger flow sharp increase and unbalanced passenger flow spatial distribution and passenger flow demands.
Disclosure of Invention
In order to overcome the technical defects, the application provides a fast and slow vehicle running optimization method for improving the running performance, which divides the types of passengers according to the route information selected by the passengers, constructs a passenger path set, calculates the passenger path selection probability, builds a double-layer planning model, finally solves the double-layer planning model by using a particle swarm algorithm, outputs an optimal running scheme, effectively improves the running performance problems of rapid increase of passenger flow and unbalanced spatial distribution of passenger flow, reduces the running cost of the train and shortens the running time of the passengers on the basis of meeting the requirements of the passengers.
The application adopts the following technical scheme:
a fast and slow vehicle driving optimization method facing to running energy lifting comprises the following steps:
s1, dividing passenger types according to the route information selected by the passengers, and constructing a passenger path set;
s2, deducing a calculation formula of influence factors of different passenger types for selecting different riding paths, and calculating passenger path selection probability by using a path comprehensive cost value formula and a Logit model according to the influence factors;
s3, establishing a double-layer planning model according to passenger flow distribution on different passenger paths, which is determined by different passenger paths and the passenger path selection probability, wherein the double-layer planning model comprises an upper model based on train stop time cost and passenger travel time cost and a lower model based on the passenger flow distribution;
and S4, solving the double-layer planning model by using a particle swarm algorithm, and outputting an optimal driving scheme.
Further, the step S1 includes the following steps:
s11, dividing passenger types according to the station types of the origin-destination points selected by the passengers, wherein the station types comprise a fast and slow vehicle stop station and a slow vehicle stop station, and the origin-destination points comprise an origin station and a destination station;
and S12, listing the possible selected riding paths of each passenger type at the same origin-destination according to the line condition, and constructing the passenger path set comprising a plurality of riding paths.
Further, the step S2 includes the following steps:
s21, defining a passenger arrival stage according to train sequencing, deducing a calculation formula of influence factors of different passenger types for selecting different riding paths in different arrival stages, and calculating the influence factors of each riding path according to the calculation formula, wherein the influence factors comprise waiting time T w Time T during vehicle s Transfer time T t ；
S22, calculating path comprehensive cost values of different riding paths by using the path comprehensive cost value formula according to the influence factors;
s23, calculating the passenger path selection probability according to the Logit model.
Further, the path comprehensive cost value formula is:
wherein,a path integrated cost value V representing a certain riding path r in the passenger path set corresponding to a certain origin-destination OD w 、V s 、V t Respectively the waiting time T w Time T during vehicle s And transfer time T t Is included in the image data.
Further, the logic model has the formula:
wherein,the passenger path selection probability of a certain passenger path r in the passenger path set corresponding to the certain origin-destination OD is represented.
Further, the step S3 includes the following steps:
s31, establishing an upper model based on an objective function, wherein the upper model comprises minimum train stop time cost and minimum passenger travel time cost;
s32, establishing a lower model for passenger flow distribution based on an objective function;
further, the upper layer model is:
wherein i and j represent line station sequences, i and j epsilon (1 and I); n represents that the driving ratio of the fast and slow vehicles is 1: n; t is t i Indicating the stop time of the train; t is t y Representing the waiting time of the overtaking slow car; alpha i Representing the 0-1 variable of the stop station of the express car; beta i Representing the override station 0-1 variable; t represents the departure interval between a fast car and a slow car;indicating the number of passengers selecting a path r when the origin-destination OD is from station i to station j; q ij Indicating that the origin-destination OD is the number of passengers from station i to station j; f (F) min Representing a constrained minimum departure interval; f (F) max Representing a constrained maximum departure interval.
Further, the lower model is;
wherein θ represents a non-negative parameter describing the randomness of the model; q ij Indicating that the origin-destination OD is the number of passengers at stations i through j.
Further, constraint conditions of the upper model comprise a fast vehicle stop constraint, a fast vehicle off-stop constraint, a departure interval constraint and a fast and slow vehicle driving proportion constraint;
the constraint condition of the lower model is passenger flow constraint, the sum of the passenger flows of different passenger paths on the same origin-destination OD is the total passenger flow on the origin-destination OD, and the passenger flow is more than or equal to 0.
Further, the step S4 includes the following steps:
s41, randomly generating a particle swarm comprising a fast car stop station, a fast car passing station, a departure interval and a fast and slow car driving proportion, wherein the particle swarm comprises a driving scheme;
s42, nesting the particle swarm into the lower model to obtain a corresponding passenger flow distribution result;
s43, nesting the passenger flow distribution result into the upper model, and solving the numerical value of the objective function;
s44, repeating the steps S41-S43, comparing the numerical value of the objective function, continuously updating the speed position of the particle swarm until the numerical value of the objective function is in a horizontal stable state, outputting an optimal particle swarm, and converting the optimal particle swarm into the expressed driving scheme to obtain the fast and slow driving optimization method.
Compared with the prior art, the application has the following beneficial technical effects:
the application provides a fast and slow vehicle driving optimization method for improving the transportation energy, which comprises the steps of dividing passenger types according to line information selected by passengers, constructing a passenger path set, deducing a calculation formula of influence factors of different passenger types for selecting different passenger paths, calculating passenger path selection probability according to the influence factors by using a path comprehensive cost value formula and a Logit model, establishing a double-layer planning model according to passenger flow distribution on different passenger paths determined by the different passenger paths and the passenger path selection probability, solving the double-layer planning model by using a particle swarm algorithm, outputting an optimal driving scheme, effectively improving the operation efficiency of an urban rail transportation system, shortening long-distance passenger travel time, effectively improving the transportation energy problems of rapid increase of passenger flow and unbalanced passenger flow space distribution, and reducing the train operation cost and the passenger time on the basis of meeting the passenger demands.
Drawings
Fig. 1 is a schematic step diagram of a fast and slow vehicle driving optimization method for improving running performance according to an embodiment of the present application.
Fig. 2 is a set of passenger paths in an embodiment of the application.
Fig. 3, fig. 4, fig. 5, fig. 6, fig. 7 are calculation formulas of waiting time, in-vehicle time and transfer time of different passenger types for selecting different paths in different arrival stages according to the present application.
Fig. 8 is a flow chart of a particle swarm algorithm solution in an embodiment of the application.
Fig. 9 is a current circuit diagram of a certain circuit in an embodiment of the present application.
Fig. 10 is a diagram of the origin-destination OD traffic data in an embodiment of the application.
Fig. 11 is an optimized layout of a circuit in an embodiment of the application.
FIG. 12 is a graph showing the comparison of the results before and after the optimization in the example of the present application.
Fig. 13, 14, 15, 16 are optimized passenger routing diagrams in an embodiment of the application.
FIG. 17 is a graph of passenger path generalized time cost and path selection probability after optimization in an embodiment of the application.
Detailed Description
For a fuller understanding of the objects, features, and effects of the present application, reference should be made to the following detailed description taken in conjunction with the accompanying drawings and detailed description.
The application discloses a fast and slow vehicle driving optimization method for improving running performance, as shown in fig. 1, which comprises the following steps:
s1, dividing passenger types according to the route information selected by the passengers, and constructing a passenger path set;
specifically, step S1 includes the steps of:
s11, dividing passenger types according to the station types of the origin and destination points selected by the passengers, wherein the station types comprise a fast and slow vehicle stop station and a slow vehicle stop station, and the origin and destination points comprise an origin station and a destination station;
and S12, listing the possible selected riding paths of each passenger type at the same origin-destination according to the line condition, and constructing a passenger path set comprising a plurality of riding paths.
In one embodiment, the passengers are classified into four types according to the types of stations selected by the passengers at the origin and destination points, wherein the AB station represents a stop of a fast car and the slow car, and the B station represents a stop of a slow car, and the B station comprises a stop of the slow car: the travel origin-destination points of the first type of passengers are all AB stops; the travel origin-destination points of the second type of passengers are respectively AB stops and B stops; the travel origin-destination points of the third type of passengers are station B and station AB respectively; the travel origin-destination of the fourth type of passenger is station B, and a set of passenger paths for different passenger types at the same origin-destination OD is shown in fig. 2.
S2, deducing a calculation formula of influence factors of different passenger types in selecting different riding paths, and calculating passenger path selection probability by using a path comprehensive cost value formula and a Logit model according to the influence factors;
specifically, the step S2 includes the following steps:
s21, defining passenger arrival stages according to train sequencing, deducing a calculation formula of influence factors of different passenger types for selecting different riding paths in different arrival stages, and calculating the influence factors of each riding path according to the calculation formula, wherein the influence factors comprise waiting time T w Time T during vehicle s Transfer time T t ；
S22, calculating path comprehensive cost values of different riding paths by using a path comprehensive cost value formula according to influence factors;
s23, calculating the passenger path selection probability according to the Logit model.
Specifically, in one implementation manner of this embodiment, taking the above different passenger types of fig. 2 as an example, the arrival phases of the passengers are defined according to the train sequencing, and calculation formulas of waiting time, in-car time and transfer time of different passenger types for selecting different paths in different arrival phases are derived according to the actual conditions of the route, where the calculation formulas corresponding to the different passenger types are as follows:
(1) when the passenger type is AB-AB (the riding section is from the ith station to the jth station), and no express station between two express stations can transfer the express car to a slow car, the corresponding calculation formulas of waiting time, in-car time and transfer time are shown in fig. 3.
(2) When the passenger type is AB-AB (the riding section is from the ith station to the jth station), and a fast station is arranged between two fast stations and can transfer a slow car, the corresponding waiting time, the corresponding in-car time and the corresponding transfer time are calculated according to the calculation formula shown in fig. 4.
(3) When the passenger type is AB-B (the riding section is from the ith station to the jth station), and a fast station is arranged between the two stations and can transfer fast and slow vehicles, the corresponding calculation formulas of waiting time, in-vehicle time and transfer time are shown in fig. 5.
(4) When the passenger type is B-AB (the riding section is from the ith station to the jth station), and a fast station is arranged between the two stations and can slowly transfer fast, the corresponding waiting time, the corresponding in-car time and the corresponding transfer time are calculated according to the calculation formula shown in fig. 6.
(5) When the passenger type is B-B (the riding section is the ith station to the jth station), the calculation formulas of the corresponding waiting time, the in-car time and the transfer time are shown in fig. 7.
The letter definitions in the figures are as follows: MZ represents the direct motion of a slow car, KZ represents the direct motion of a fast car, M-K represents the transfer of the slow car to the fast car, and K-M represents the transfer of the fast car to the slow car; n represents that the driving ratio of the fast and slow vehicles is 1: n; r is (r) i Representing interval run time; t is t i Indicating the stop time of the train; t is t y Representing the waiting time of the overtaking slow car; alpha i Representing the 0-1 variable of the stop station of the express car; beta i Representing the override station 0-1 variable; t represents the departure interval between the fast car and the slow car.
Secondly, three factors influencing the selection path of the traveler, namely waiting time T, are comprehensively considered w Time T during vehicle s Transfer time T t The path comprehensive cost value formula is as follows by utilizing the random utility principle:
wherein,a path integrated cost value V representing a certain riding path r in the passenger path set corresponding to a certain origin-destination OD w 、V s 、V t Respectively the waiting time T w Time T during vehicle s And transfer time T t Is included in the image data.
The Logit model is used for solving the probability of passenger path selection, and the determinable utility is represented by the generalized cost of the path, and the formula of the Logit model is as follows:
wherein,the passenger path selection probability of a certain passenger path r in the passenger path set corresponding to the certain origin-destination OD is represented.
S3, establishing a double-layer planning model according to passenger flow distribution on different passenger paths, which is determined by different passenger paths and passenger path selection probabilities, wherein the double-layer planning model comprises an upper model based on train stop time and passenger travel time cost and a lower model based on passenger flow distribution;
specifically, step S3 includes the following steps:
s31, establishing an upper model based on an objective function, wherein the upper model comprises minimum train stop time cost and minimum passenger travel time cost;
s32, establishing a lower model for passenger flow distribution based on an objective function;
specifically, the upper layer model is:
wherein i and j represent line station sequences, i and j epsilon (1 and I); n represents that the driving ratio of the fast and slow vehicles is 1: n; t is t i Indicating the stop time of the train; t is t y Representing the waiting time of the overtaking slow car; alpha i Representing the 0-1 variable of the stop station of the express car; beta i Representing the override station 0-1 variable; t represents the departure interval between a fast car and a slow car;indicating the number of passengers selecting a path r when the origin-destination OD is from station i to station j; q ij Indicating that the origin-destination OD is the number of passengers from station i to station j; f (F) min Representing a constrained minimum departure interval; f (F) max Representing a constrained maximum departure interval.
The objective of the upper model is to optimize the driving organization scheme, and the objective function is to pursue the minimization of the operation cost and the minimization of the passenger travel time cost from the consideration of the enterprise operation cost and the passenger travel time cost. From the analysis of the train operation angle, the enterprise operation cost comprises the running kilometer cost of the train, the purchase cost of the train and the stop cost of the train, the running mileage per train and the purchase cost of the train are basically unchanged under the condition of fixed running scheme, and the stop cost of the train is changed along with the stop scheme of the train, so that the enterprise operation cost only considers the stop time of the train in unit hour, and the minimum travel time of passengers in unit hour on all paths is pursued from the aspect of passengers.
Specifically, the lower model is;
wherein θ represents a non-negative parameter describing the randomness of the model; q ij Indicating that the origin-destination OD is the number of passengers at stations i through j.
The lower model is a random user balance optimization model based on passenger flow distribution, and the model has been proved to meet the passenger flow distribution random balance condition of the Logit model.
Specifically, constraint conditions of the upper model include a fast vehicle stop constraint, a fast vehicle crossing stop constraint, a departure interval constraint and a fast and slow vehicle driving proportion constraint, and meanwhile, one fast vehicle can only cross a slow vehicle once, and the crossing operation can only occur at a slow station; the constraint condition of the lower layer model is passenger flow constraint, the sum of the passenger flows of different riding paths on the same origin-destination OD is the total passenger flow on the origin-destination OD, and the passenger flow is more than or equal to 0.
And S4, solving the double-layer planning model by using a particle swarm algorithm, and outputting an optimal driving scheme.
Specifically, step S4 includes the steps of:
s41, randomly generating a particle swarm comprising a fast car stop station, a fast car passing station, a departure interval and a fast and slow car driving proportion, wherein the particle swarm comprises a driving scheme;
s42, nesting the particle swarm into a lower model to obtain a corresponding passenger flow distribution result;
s43, nesting the passenger flow distribution result into an upper model, and solving the numerical value of an objective function;
s44, repeating the steps S41-S43, comparing the numerical values of the objective function, continuously updating the speed position of the particle swarm until the numerical values of the objective function are in a horizontal stable state, outputting the optimal particle swarm, converting the optimal particle swarm into the expressed driving scheme, and obtaining the fast and slow driving optimization method, wherein a particle swarm algorithm solving flow chart in the embodiment of the application is shown in FIG. 8.
Specifically, application analysis is performed by taking a certain line as an example, and the line information is shown in fig. 9. In the actual driving scheme, the driving ratio of the fast and slow vehicles is 1:4, and the vehicles are not equally spaced, and the range is approximately 4-10min. And (5) sorting out the 17:30-18:30 late peak OD data in the uplink direction by adopting the passenger flow data of xxxx, xx, month and xx days, wherein the OD data is shown in fig. 10.
The running scheme after the parameters of the brought lines and the passenger flow information are optimized according to the optimization model is shown in fig. 11. The driving ratio of the fast and slow vehicles is 1:3, the departure interval between the slow vehicles is about 8'20s, the departure interval between the fast vehicles and the slow vehicles is about 4'40s, and the difference between the fast and slow vehicles and the scheme before optimization is not great.
The result pairs before and after optimization are shown in fig. 12. From the perspective of passengers, the travel time of the passengers is reduced after optimization, the waiting time of the passengers after optimization is reduced from 6067859s to 5335136s, 732523s is reduced, and the time saving percentage is 12.1%; from the operation point of view, the train stop cost per unit time is increased from 5372s to 5520s, the stop cost is increased by 148s, but the average stop cost of the train is about 575s, because the line passing capacity is improved after optimization, the number of the lines passing per unit time is increased, the total stop cost of the train is increased, but the average stop cost of the train is reduced by 49.7s and about 8 percent relative to the scheme before optimization, and the optimization effect is also achieved; the line passing capacity is increased from 8.5 columns/h to 9.6 columns/h, the unit time is increased by 1 column, the operation capacity is improved by 11.6%, and a larger optimization effect is obtained.
The optimal passenger flow distribution scheme in the driving scheme is also obtained in the lower layer model, wherein representative four types of OD generation path diagrams are shown in fig. 13, 14, 15 and 16, and generalized time cost and path selection probability are shown in fig. 17.
The embodiment provides a fast and slow vehicle driving optimization method for improving the running performance, which comprises the steps of dividing passenger types according to line information selected by passengers, constructing a passenger path set, deducing a calculation formula of influence factors of different passenger types for selecting different riding paths, calculating passenger path selection probability according to the influence factors by using a path comprehensive cost value formula and a Logit model, establishing a double-layer planning model according to passenger flow distribution on different riding paths and the passenger path selection probability, solving the double-layer planning model by using a particle swarm algorithm, outputting an optimal running scheme, effectively improving the running efficiency of an urban rail transit system, shortening long-distance passenger running time, effectively improving the running performance problems of rapid increase of passenger flow and unbalanced passenger flow space distribution, and reducing the running cost of a train and the running time of the passengers on the basis of meeting the passenger demands.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-viewable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk, etc.
While the preferred embodiments of the present application have been described in detail, it should be understood that numerous modifications and variations can be made in accordance with the concepts of the application by those skilled in the art without undue burden. Therefore, all technical solutions which can be obtained by logic analysis, reasoning or according to limited experiments on the basis of the prior art by a person skilled in the art according to the inventive concept shall be within the scope of protection defined by the claims.
Claims (8)
1. The fast and slow vehicle driving optimization method for the operation energy improvement is characterized by comprising the following steps of:
s1, dividing passenger types according to the route information selected by the passengers, and constructing a passenger path set;
s2, deducing a calculation formula of influence factors of different passenger types for selecting different riding paths, and calculating passenger path selection probability by using a path comprehensive cost value formula and a Logit model according to the influence factors; the Logit model formula is:
wherein,a passenger route selection probability indicating a passenger route r in a passenger route set corresponding to a certain origin-destination OD, +.>A path integrated cost value indicating a certain riding path r in the passenger path set corresponding to a certain origin-destination OD;
s3, establishing a double-layer planning model according to passenger flow distribution on different passenger paths, which is determined by different passenger paths and the passenger path selection probability, wherein the double-layer planning model comprises an upper model based on train stop time cost and passenger travel time cost and a lower model based on the passenger flow distribution; the upper layer model is as follows:
wherein i and j represent line station sequences, i and j epsilon (1 and I); n represents that the driving ratio of the fast and slow vehicles is 1: n; t is t i Indicating the stop time of the train; t is t y Representing the waiting time of the overtaking slow car; alpha i Representing the 0-1 variable of the stop station of the express car; beta i Representing the override station 0-1 variable; t represents the departure interval between a fast car and a slow car; f (f) r ij Indicating the number of passengers selecting a path r when the origin-destination OD is from station i to station j; q ij Indicating that the origin-destination OD is the number of passengers from station i to station j; f (F) min Representing a constrained minimum departure interval; f (F) max Representing a constrained maximum departure interval;
and S4, solving the double-layer planning model by using a particle swarm algorithm, and outputting an optimal driving scheme.
2. The method for optimizing the driving of the fast and slow vehicles for improving the running performance according to claim 1, wherein the step S1 comprises the following steps:
s11, dividing passenger types according to the station types of the origin-destination points selected by the passengers, wherein the station types comprise a fast and slow vehicle stop station and a slow vehicle stop station, and the origin-destination points comprise an origin station and a destination station;
and S12, listing the possible selected riding paths of each passenger type at the same origin-destination according to the line condition, and constructing the passenger path set comprising a plurality of riding paths.
3. The method for optimizing the driving of the fast and slow vehicles for improving the running performance according to claim 2, wherein the step S2 comprises the following steps:
s21, defining a passenger arrival stage according to train sequencing, deducing a calculation formula of influence factors of different passenger types for selecting different riding paths in different arrival stages, and calculating the influence factors of each riding path according to the calculation formula, wherein the influence factors comprise waiting time T w Time T during vehicle s Transfer time T t ；
S22, calculating path comprehensive cost values of different riding paths by using the path comprehensive cost value formula according to the influence factors;
s23, calculating the passenger path selection probability according to the Logit model.
4. The method for optimizing the driving of the fast and slow vehicles for improving the operation performance according to claim 3, wherein,
the path comprehensive cost value formula is as follows:
wherein V is w 、V s 、V t Respectively the waiting time T w Time T during vehicle s And transfer time T t Is included in the image data.
5. The method for optimizing the driving of the vehicle for improving the driving performance according to claim 4, wherein the step S3 comprises the following steps:
s31, establishing an upper model based on an objective function, wherein the upper model comprises minimum train stop time cost and minimum passenger travel time cost;
s32, establishing a lower model for passenger flow distribution based on the objective function.
6. The method for optimizing the driving of the fast and slow vehicles for improving the operation performance according to claim 5, wherein,
the lower model is;
wherein θ represents a non-negative parameter describing the randomness of the model; q ij Indicating that the origin-destination OD is the number of passengers at stations i through j.
7. The method for optimizing the driving of the fast and slow vehicles for improving the operation performance according to claim 6, wherein,
constraint conditions of the upper model comprise fast vehicle stop constraint, fast vehicle off-road constraint, departure interval constraint and fast and slow vehicle driving proportion constraint;
the constraint condition of the lower model is passenger flow constraint, the sum of the passenger flows of different passenger paths on the same origin-destination OD is the total passenger flow on the origin-destination OD, and the passenger flow is more than or equal to 0.
8. The method for optimizing the driving of the vehicle for improving the driving performance according to claim 7, wherein the step S4 comprises the following steps:
s41, randomly generating a particle swarm comprising a fast car stop station, a fast car passing station, a departure interval and a fast and slow car driving proportion, wherein the particle swarm comprises a driving scheme;
s42, nesting the particle swarm into the lower model to obtain a corresponding passenger flow distribution result;
s43, nesting the passenger flow distribution result into the upper model, and solving the numerical value of the objective function;
s44, repeating the steps S41-S43, comparing the numerical value of the objective function, continuously updating the speed position of the particle swarm until the numerical value of the objective function is in a horizontal stable state, outputting an optimal particle swarm, and converting the optimal particle swarm into the expressed driving scheme to obtain the fast and slow driving optimization method.
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